Physical Sciences and Mathematics Commons^™

Rscore - A Tool For Learning And Quantifying Software Infrastructure Resiliency, Selena Yujia Zhou

Computer Science Senior Theses

As LLMs take over writing code, infrastructure resilience has become the central challenge of software development, especially for gaming applications, where latency, availability, and scale demands are extreme. However, holistic software infrastructure is difficult to understand for casual developers because of the multiple layers of abstraction that make up an application, and the lack of structured information from proprietary companies. This thesis explores the creation of rscore, a tool that quantifies software infrastructure resiliency and subsequently educates developers about the architecture of any existing gaming application. The tool generates two graphical models of a game’s early versus current infrastructure and …

Ai, Translation, And Telling The Truth, David I. Smith

University Faculty Publications and Creative Works

I am working on a large translation project this year. I have been surprised to find several conversation partners voicing the assumption that I am getting AI to do the translating for me. I’ve been wondering how to respond.

A short, but in the end inadequate answer is that, impressive as the current variations on machine translation are, they still get things wrong. Neural machine translation services such as Google Translate and DeepL still produce oddities fairly regularly. I have been working lately with seventeenth-century Czech texts, an area in which I would expect machine translation to struggle a little …

Holistic Strategies For Optimizing Municipal It Operations And Cost Efficiency, Joseph Yazdanpanahi

Certified Public Manager® Applied Research

Municipalities work to deliver secure and impactful IT (information technology) services while often working within tight budget constraints. To address this problem, this article offers an experience-based and comprehensive framework to optimize municipal IT operations. It emphasizes leveraging automation, streamlining hardware and software management, and forging strategic partnerships to drive cost savings and efficiency gains. Designed to equip municipal IT leaders with practical tools and insights, this article aims to help modernize IT processes.

Residential Ai Data Centers: Security, Privacy, And Governance Concerns, Alan Saquella

Publications

The concept of placing mini data centers and distributed AI computer nodes inside residential homes may appear innovative from an energy efficiency perspective, but it introduces significant security, privacy, governance, and liability concerns. What is effectively occurring is the expansion of commercial and potentially critical infrastructure into lightly protected residential environments.

Once a residence becomes part of a distributed computer grid supporting hyper-scalers, AI providers, or enterprise workloads, the home is no longer simply a private residence. It becomes a commercial technology asset, a potential cyber target, and even a physical target. A distributed network of thousands of residential nodes …

A Comprehensive Survey Of Artificial Intelligence Applications In Predicting Mining-Induced Subsidence, Deformation, And Landslides: Strengths, Limitations, And Future Trends, Long Quoc Nguyen, Dung Ba Nguyen, Minh Tuyet Dang

Journal of Sustainable Mining

Mining activities often cause mining-induced ground deformation, including subsidence and landslides, and related geo-environmental impacts, posing significant risks to infrastructure and safety. This study conducts a systematic assessment to identify, categorize, and evaluate AI-based methods (machine learning, deep learning, and hybrid models) for predicting and monitoring mining-induced ground deformation. The literature search was performed across major scientific databases, using predefined keywords and selection criteria, resulting in a final dataset of relevant peer-reviewed studies. The reviewed works were classified into three methodological groups: traditional machine learning, deep learning-based approaches, and hybrid methods. The results show that ML still dominates in terms …

On The Fractional Laplacian Type Operator, Maysam Abdulnaser Zain

Thesis/ Dissertation Defenses

In this thesis, we study analytical structures arising from Dunkl theory and their applications to harmonic analysis and fractional Laplacian operators. Dunkl operators are differential-difference operators associated with finite reflection groups, providing a natural generalization of the classical Fourier analysis through the introduction of root systems and multiplicity functions. Within this framework, several classical transforms appear as special cases of the (k,a)-generalized Fourier transform. We study the generalized Fourier transform, its kernel, and the associated translation operator and convolution structures. Using these tools, we construct the corresponding heat kernel and analyze the associated heat semigroup. Our main contribution concerns the …

Structure-Property Relationship In Low-Dimensional Cesium Metal Halides: Coordination Chemistry, Luminescent Mechanism And Optoelectronics Applications, Sheikh Jobe

Thesis/ Dissertation Defenses

Low-dimensional metal halides are gaining popularity in optoelectronic applications owing to their interesting optical properties and structural diversity. In general, low-dimensional metal halides refer to one-dimensional and zero-dimensional structures. The main difference between the two is that one-dimensional structures have chain-like connectivity, whereas zero-dimensional structures consist of isolated units. Their optical properties are mainly determined by the B-site metal ion, which is coordinated to halide ions in trigonal, tetrahedral, or octahedral geometry. In particular, copper (I) and manganese (II) metal halides are emerging as novel materials for solid-state lighting, and anti-counterfeiting applications. The two well-known phases of cesium copper (I) …

Decoding Stock Market Movements: The Role Of Unemployment And Volatility, Bipllab Roy, Suparna Bhattacharjee

Northeast Journal of Complex Systems (NEJCS)

This study investigates how unemployment and market volatility interact with stock prices in the Indian context, framing the stock–labour–volatility nexus as a complex adaptive system (CAS) rather than a set of linear, time-invariant relationships. Using verified secondary data on unemployment, India VIX, and NSE stock indices for 2013–2023, we first apply simple and multiple regression as a descriptive baseline. Results show a strong negative association between unemployment and stock prices (R ≈ 0.824, R² ≈ 0.68, p < 0.05), consistent with Keynesian demand-side channels, while the linear VIX–stock relationship is weak and statistically insignificant (R² ≈ 0.07, p > 0.05), consistent with the expectation that volatility operates through non-linear, regime-dependent mechanisms not captured by OLS.

Importantly, we document and transparently disclose critical …

Mathematical Analysis Of Within-Host Models: Viral–Immune Dynamics, Bifurcations, And Disease Severity, Nazia Afrin

Doctoral Dissertations

My research develops and analyzes ODE-based within-host models at multiple scales. Using dynamical systems theory and numerical methods, I study host–pathogen interactions and immune responses, providing insights into disease dynamics and control. The first model describes the complex dynamics of Hepatitis B virus (HBV) infection and addresses the question: what mechanisms determine whether the infection is cleared during the acute phase or progresses to a chronic state? A key feature of this model is the assumption that all classes of liver cells (uninfected, infected, and protected from reinfection) proliferate at different rates. The findings provide insight into two important aspects …

The Impact Of Evolution And Strong Allee Effects On The Dynamics Of A Discrete-Time Predator-Prey System, Neerob Basak

Doctoral Dissertations

This dissertation investigates the dynamics of discrete-time predator-prey systems, focusing on both evolutionary responses and ecological interactions. The first part of this dissertation, in Chapters 2 and 3, explores how evolutionary processes, particularly the development of resistance to toxicants in predators, influence the persistence and stability of predator-prey populations. In this part, we extend the predator-prey model developed in Ackleh et al., 2019 to incorporate the evolution of a predator's resistance to toxicant effects. We consider three cases: (1) lethal effects, where the toxicant directly influences the predator's survival; (2) sublethal effects, where the toxicant impacts the predator's fecundity, and …

A Bifurcation Theorem And Its Application To Discrete-Time Models In Ecology And Epidemiology, Jenita Jahangir

Doctoral Dissertations

Matrix models are useful for modeling populations or diseases that involve discrete developmental stages, multiple stages of infection, and interactions among species. To study the coexistence dynamics in matrix models, we extend a bifurcation theorem for resident-invader host-parasitoid type populations by allowing every block of the projection matrix, depending on the bifurcation parameter and the off-diagonal blocks, to be nonzero. As an application, in the first part of the dissertation, we propose a discrete-time host-parasitoid model with stage structure in both species. For this model, we establish conditions for the existence and global stability of the extinction and parasitoid-free equilibria. …

Fairness-Aware And Efficient Federated Learning Frameworks For Heterogeneous Systems, Simin Javaherian

Doctoral Dissertations

Federated Learning (FL) enables decentralized clients to collaboratively train machine learning models without sharing raw data, making it a promising paradigm for privacy-preserving intelligence across large-scale, heterogeneous systems. However, practical FL environments face significant challenges arising from variations in client resources, participation patterns, client behavior, and data distributions. These challenges often lead to inefficiency, unbalanced contributions, and unfairness, ultimately degrading model performance and discouraging long-term client participation. This dissertation advances the state of FL by developing a unified suite of fairness-aware and efficiency-driven frameworks tailored for heterogeneous environments. We investigate fairness from multiple perspectives, including client selection, contribution weighting, and …

Strang-Type Exponential Integrators For Stiff Reaction-Diffusion Systems, Saburi Tolulope Rasheed

Doctoral Dissertations

Reaction-diffusion systems, as examples of semilinear parabolic partial differential equations, have played significant roles in the mathematical modeling of physical, chemical, and biological processes. Several reaction-diffusion systems typically do not have exact solutions in closed form, and numerically solving them also comes with challenges due to the presence of the nonlinear local interaction/chemical reaction dynamics representing the reaction term, coupling between components, multidimensionality of the diffusion operator, and stiffness of the diffusion and/or reaction terms. Wederive and analyze several second-order accurate exponential integrators of the Strang type for the time discretization of stiff reaction-diffusion systems. We utilize the finite difference …

The Interplay Of Seasonality, Evolution, And Density-Dependence In Discrete-Time Predator-Prey Dynamics, Narendra Pant

Doctoral Dissertations

We extend the discrete-time mathematical models developed in (Ackleh et al., 2019) and (Ackleh et al., 2024) to account for seasonal prey reproduction and build a class of discrete-time predator-prey seasonal models. Each model distinguishes between breeding and non-breeding seasons, representing prey reproduction as a periodic function of period 2. Altogether, three different models are analyzed. In the first part, we extend the predator-prey model from (Ackleh et al., 2019) to incorporate seasonality. We study the resulting dynamics and show that when the inherent reproduction number of the prey and the invasion reproduction number of the predator are larger than …

Building And Restoring Trust In Deep Learning: From Multimodal Sensing To Generative Synthesis And Model Integrity, Liqun Shan

Doctoral Dissertations

Deep learning has become a foundational technology for modern intelligent systems used in sensing, authentication, media generation, and automated decision-making. As these systems are increasingly deployed in security- and privacy-sensitive settings, ensuring their trustworthiness has become a critical challenge. Yet deep learning models remain vulnerable to spoofed sensory inputs, synthetic media, and malicious behaviors hidden within trained networks. These vulnerabilities undermine reliability and raise serious concerns about whether such systems can be trusted under adversarial and deceptive scenarios. This dissertation investigates how to build and restore trust in deep learning across three tightly connected dimensions: multimodal sensing, generative authenticity, and …

Learning And Predicting The Performance Of Gradual Type System, Mohammad Wahiduzzaman Khan

Doctoral Dissertations

Gradual typing reconciles the complementary strengths of static and dynamic typing by allowing programmers to incrementally introduce type annotations while preserving the flexibility of dynamically typed code. This approach improves reliability, documentation, and tooling support without sacrificing rapid prototyping. To ensure soundness, gradual type systems enforce annotations through runtime checks, typically implemented via cast insertion. Although these checks guarantee correctness, they can introduce substantial and highly variable runtime overhead, making performance prediction and optimization a central challenge for practical adoption. Prior work has largely focused on coarse-grained (macro-level) configurations, where entire modules are either fully typed or untyped. While such …

Investigating The Distribution And Origin Of Pitted Mounds On Mars Using Machine Learning-Based Mapping And Integrated Geological Analysis: Application To Isidis Planitia, Precious Batubo

Doctoral Dissertations

Pitted mounds are widespread landforms across the northern plains of Mars, yet their origin remains uncertain. These features have been interpreted as possible expressions of subsurface fluid activity, including sedimentary volcanism, magmatic processes, or other fluid-assisted mechanisms. Determining their distribution, morphology, and geology is therefore important for understanding the evolution of subsurface hydrological processes and the planet’s potential for habitability. However, the large spatial extent of mound-bearing terrains makes comprehensive manual mapping impractical. This dissertation presents an automated approach to mound detection using Faster Region-Based Convolutional Neural Network (Faster R-CNN) from high-resolution Context Camera (CTX) images including morphometric and mineralogical …

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Emerging Catalysts And System Designs For High-Current-Density Co2 Electroreduction To Multicarbon Fuels And Chemicals Toward Industrial-Scale Applications, Godwin Uche Edor

Masters Theses

Electrochemical CO2 reduction (eCO2RR) offers a sustainable route to convert CO2 into value-added fuels and chemicals, particularly multicarbon (C2+) products due to their higher energy density and economic value compared to C1 counterparts. Industrial adoption of eCO2RR technology is hindered by the difficulty in stably reaching superior Faradaic efficiency (FE) for C2+ products at ≥ 200 mA cm-2 at low applied overpotential due to sluggish C–C coupling kinetics, competing reactions, and catalyst instability. Hence, we examine the recent advances in eCO2RR catalyst engineering strategies, such as nanostructural and electronic modifications of Cu-based catalysts (e.g., interfacial, surface, facet, and defect engineering, …

Micro-Hyperspectral Imaging Of Phytoplankton Enables Deconvolution Of Mixed Communities For Spaceborne Hyperspectral Remote Sensing, Chisom Okwuchi Emeghiebo

Masters Theses

Characterizing phytoplankton diversity is critical for large-scale biodiversity monitoring and harmful algal bloom (HAB) detection. Here, we present a micro hyperspectral imaging method to characterize reflectance signatures, for the first time across major phytoplankton groups. Spectral variability among taxa in laboratory cultures is interpreted using algal pigment and absorption analyses, demonstrating that pigment composition and absorption govern reflectance features. Chlorophytes and cryptophytes exhibit distinct spectral signatures and are readily distinguishable, whereas diatoms and dinoflagellates show greater spectral similarity, with differentiation relying on subtle reflectance features associated with chlorophyll c₁c₂ near ~463 nm and within 620–650 nm. We further applied this …

Linking Greenhouse Gas Fluxes And Plant Responses To Saltwater Intrusion Events Using Remote Sensing, Madeline Jean Moore

Masters Theses

As sea level rise and coastal subsidence increase coastal Louisiana's vulnerability to saltwater intrusion (SWI) events, accurate and rapid monitoring of vegetation responses to these events is becoming increasingly important. In this study, we utilized unmanned aerial vehicle (UAV) multispectral imagery to assess short-term vegetation responses to simulated SWI events in the Visser’s Experimental Wetland Complex in Saint Martinville, Louisiana. Vegetation patches of two common freshwater wetland species, Panicum hemitomon (Maidencane) and Typha domingensis (Southern Cattail), were exposed to 5 ppt salinity for 6-, 10-, and 17-day durations. Multispectral imagery of both species was collected before and after each SWI …

Classifying Storm Surges Along Louisiana Coast Using Machine Learning, Faith Ewere Okunbor

Masters Theses

Storm surges are among the most significant threats to coastal wetlands, causing flooding and saltwater intrusion that alter hydrology, vegetation structure, and long-term ecosystem resilience. Along the Louisiana coast, storm surge effects vary depending on storm behavior, shoreline geometry, and marsh hydrologic connectivity, yet are commonly characterized using broad storm metrics rather than site-specific environmental responses. This study develops a data-driven framework to classify storm surges across coastal Louisiana wetlands using observations from the Coastwide Reference Monitoring System (CRMS). Hourly water-level and salinity time series from twelve hurricanes (2007-2024) were analyzed using a 40-day storm-centered window. Hydrologic and salinity metrics …

Regioselective Tellurination Reactions And Their Utilities, Itunu Comfort Olanrewaju

Masters Theses

This thesis provides a comprehensive study of regioselective tellurination and its applications by integrating experimental and computational techniques, with an aim of synthesizing libraries of organotellurium heterocycles. This study addresses existing challenges in regioselective tellurination, such as unpredictable regioselectivity and avoidance of toxic reagents by harnessing intramolecular Te…O coordination and electrophilic aromatic tellurination. This strategy enables the development of previously poorly accessible compounds including benzotellurazinones, 2-acylaminobenzotellurazoles, and previously unknown 2,3-dihydrobenzo [b] oxatelluranes, which were prepared by antiMarkovnikov anti-addition of TeCl4 to C-C triple bonds. Experimental studies were complemented by computational DFT analyses to provide mechanistic and energetic insight into the …

Stratigraphic Distribution Of Pleistocene Aves At Fossil Lake In Lake County, Oregon, Cole Bradley Phillips

Masters Theses

The Fossil Lake locality in south-central Oregon is rich in many species of fossil remains and is also home to packages of sandstone that grade upward into finer siltstone or claystone. These packages have been interpreted to represent rising and falling lake levels as a result of glacial transgressive and regressive sequences. The fossil vertebrates and invertebrates have also fluctuated because of the different climates at Fossil Lake. Although the stratigraphic distribution of most classes of animals have been determined, fossil birds have not. Here, the distribution of 329 avian fossils is presented in the stratigraphic framework. The species were …